Performance and cryptographic evaluation of security protocols in distributed networks using applied pi calculus and Markov Chain

The development of cryptographic protocols goes through two stages, namely, security verification and performance analysis. The verification of the protocol’s security properties could be analytically achieved using threat modelling, or formally using formal methods and model checkers. The performance analysis could be mathematical or simulation-based. However, mathematical modelling is complicated and does not reflect the actual deployment environment of the protocol in the current state of the art. Simulation software provides scalability and can simulate complicated scenarios, however, there are times when it is not possible to use simulations due to a lack of support for new technologies or simulation scenarios. Therefore, this paper proposes a formal method and analytical model for evaluating the performance of security protocols using applied pi-calculus and Markov Chain processes. It interprets algebraic processes and associates cryptographic operatives with quantitative measures to estimate and evaluate cryptographic costs. With this approach, the protocols are presented as processes using applied pi-calculus, and their security properties are an approximate abstraction of protocol equivalence based on the verification from ProVerif and evaluated using analytical and simulation models for quantitative measures. The interpretation of the quantities is associated with process transitions, rates, and measures as a cost of using cryptographic primitives. This method supports users’ input in analysing the protocol’s activities and performance. As a proof of concept, we deploy this approach to assess the performance of security protocols designed to protect large-scale, 5G-based Device-to-Device communications. We also conducted a performance evaluation of the protocols based on analytical and network simulator results to compare the effectiveness of the proposed approach

Information fusion-based cybersecurity threat detection for intelligent transportation system

Intelligent Transportation Systems (ITS) are sophisticated systems that leverage various technologies to increase the safety, efficiency, and sustainability of transportation. By relying on wireless communication and data collected from diverse sensors, ITS is vulnerable to cybersecurity threats. With the increasing number of attacks on ITS worldwide, detecting and addressing cybersecurity threats has become critically important. This need will only intensify with the impending arrival of autonomous vehicles. One of the primary challenges is identifying critical ITS assets that require protection and understanding the vulnerabilities that cyber attackers can exploit. Additionally, creating a standard profile for ITS is challenging due to the dynamic traffic pattern, which exhibits changes in the movement of vehicles over time. To address these challenges, this paper proposes an information fusion-based cybersecurity threat detection method. Specifically, we employ the Kalman filter for noise reduction, Dempster-Shafer decision theory and Shannon’s entropy for assessing the probabilities of traffic conditions being normal, intruded, and uncertain. We utilised Simulation of Urban Mobility (SUMO) to simulate the Melbourne CBD map and historical traffic data from the Victorian transport authority. Our simulation results reveal that information fusion with three sensor data is more effective in detecting normal traffic conditions. On the other hand, for detecting anomalies, information fusion with two sensor data is more efficient

IoT-based emergency vehicle services in intelligent transportation system

Emergency Management System (EMS) is an important component of Intelligent transportation systems, and its primary objective is to send Emergency Vehicles (EVs) to the location of a reported incident. However, the increasing traffic in urban areas, especially during peak hours, results in the delayed arrival of EVs in many cases, which ultimately leads to higher fatality rates, increased property damage, and higher road congestion. Existing literature addressed this issue by giving higher priority to EVs while traveling to an incident place by changing traffic signals (e.g., making the signals green) on their travel path. A few works have also attempted to find the best route for an EV using traffic information (e.g., number of vehicles, flow rate, and clearance time) at the beginning of the journey. However, these works did not consider congestion or disruption faced by other non-emergency vehicles adjacent to the EV travel path. The selected travel paths are also static and do not consider changing traffic parameters while EVs are en route. To address these issues, this article proposes an Unmanned Aerial Vehicle (UAV) guided priority-based incident management system to assist EVs in obtaining a better clearance time in intersections and thus achieve a lower response time. The proposed model also considers disruption faced by other surrounding non-emergency vehicles adjacent to the EVs’ travel path and selects an optimal solution by controlling the traffic signal phase time to ensure that EVs can reach the incident place on time while causing minimal disruption to other on-road vehicles. Simulation results indicate that the proposed model achieves an 8% lower response time for EVs while the clearance time surrounding the incident place is improved by 12%

Efficient design for smart environment using Raspberry Pi with Blockchain and IoT (BRIoT)

Internet of Things (IoT) is reshaping digital world day by day by integrating several technologies to provide smart services. However, intrinsic features of IoT resulting in a number of challenges, such as decentralization, poor interoperability, privacy, confidentiality, and security vulnerabilities. Several security techniques like encryption, third-party software’s are in use currently to protect users data. Blockchain was initially established for digital crypto currencies with a Proof of Work (PoW) consensus process and the advantage of smart contracts, which enabled distributed trust without the involvement of a third party. Its distributed trust concept paved the way for many other developments, such as the development of new consensus mechanisms such as Proof of Stake (PoS) and Proof of Authority (PoA), which aided in the adoption of Blockchain with low computation machines into sectors such as smart industry and smart transportation. Blockchain implementation in IoT can address the security issue, here we proposed a design using Raspberry Pi as edge node (BRIoT)

Federated learning for performance prediction in multi-operator environments

Telecom vendors and operators deliver services with strict requirements on performance, over complex and sometimes partly shared network infrastructures. A key enabler for network and service management in such environments is knowledge sharing, and the use of data-driven models for performance prediction, forecasting, and troubleshooting. In this paper, we outline a multi-operator service metrics prediction framework using federated learning that allows privacy-preserved knowledge-sharing across operators for improved model performance, and also reduced requirements on data transfer within an operator network. Federated learning is compared against local and central learning strategies for multi-operator performance prediction, and it is shown to balance the requirements on data privacy, model performance, and the network overhead. Further, the paper provides insights on how data heterogeneity affects model performance, where the conclusion is that standard federated learning has certain robustness to data heterogeneity. Finally, we discuss the challenges related to training a federated learning model with a limited budget on the communication rounds. The evaluation is performed using a set of realistic publicly available data traces, that are adapted specifically for the purpose of studying multi-operator service performance prediction

Leveraging oversampling techniques in machine learning models for multi-class malware detection in smart home applications

Smarthome applications are becoming increasingly popular due to their ability to provide safety, comfort, and remote assistance. These applications are usually controlled using a smart home controller, which is often the target of malware attacks. A successful attack may result in financial loss, disclosure of personal and/or sensitive information, or even loss of human lives. Although machine learning models have been used in existing research for detecting multi-class malware attacks in smart home systems, they did not explicitly address the class imbalance problem in such cases. In addition, the use of ensemble learner is expected to provide improved performance. To address this, we investigated different oversampling techniques to increase the number of samples in the minority classes and incorporated ensemble learners to see their impact on the prediction performance. Experimental evaluation shows significant improvements (4-5%) in terms of accuracy, precision, recall, and F-1 score. Index Terms—Oversampling Techniques, Ensemble Models, Multi-class Malware Detectio